{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tests ForecasterRecursiveMultiSeries when series have the same length without NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/varios/skforecast\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.12.0 | packaged by Anaconda, Inc. | (main, Oct  2 2023, 17:29:18) [GCC 11.2.0]\n",
      "skforecast version: 0.12.0\n",
      "lightgbm version: 3.3.5\n",
      "sklearn version: 1.3.2\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import sys\n",
    "from pathlib import Path\n",
    "path = str(Path.cwd().parent.parent)\n",
    "sys.path.insert(1, path)\n",
    "print(path)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "from skforecast.plot import set_dark_theme\n",
    "from tqdm.notebook import tqdm\n",
    "import skforecast\n",
    "import lightgbm\n",
    "import sklearn\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "from skforecast.preprocessing import series_long_to_dict\n",
    "from skforecast.preprocessing import exog_long_to_dict\n",
    "from skforecast.recursive import ForecasterRecursiveMultiSeries\n",
    "from skforecast.model_selection_multiseries import backtesting_forecaster_multiseries\n",
    "from skforecast.model_selection_multiseries import (\n",
    "    bayesian_search_forecaster_multiseries,\n",
    ")\n",
    "from skforecast.model_selection_multiseries import grid_search_forecaster_multiseries\n",
    "import warnings\n",
    "import sys\n",
    "\n",
    "print(f\"Python version: {sys.version}\")\n",
    "print(f\"skforecast version: {skforecast.__version__}\")\n",
    "print(f\"lightgbm version: {lightgbm.__version__}\")\n",
    "print(f\"sklearn version: {sklearn.__version__}\")\n",
    "print(f\"Last execution: {pd.Timestamp.now()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load time series same length\n",
    "# ==============================================================================\n",
    "data = pd.read_parquet(\"../fixtures/sample_building_consumption.parquet\")\n",
    "data = data.asfreq(\"D\")\n",
    "series = data[[\"id_1000\", \"id_1001\", \"id_1002\", \"id_1003\", \"id_1004\"]]\n",
    "exog = data[[\"sin_day_of_week\", \"cos_day_of_week\", \"air_temperature\", \"wind_speed\"]]\n",
    "\n",
    "end_train = \"2016-10-31 23:59:00\"\n",
    "series_train = series.loc[:end_train, :].copy()\n",
    "exog_train = exog.loc[:end_train, :].copy()\n",
    "series_test = series.loc[end_train:, :].copy()\n",
    "exog_test = exog.loc[end_train:, :].copy()\n",
    "\n",
    "series_dict = series.to_dict(orient=\"series\")\n",
    "exog_dict = {k: exog for k in series.columns}\n",
    "series_dict_train = series_train.to_dict(orient=\"series\")\n",
    "exog_dict_train = {k: exog_train for k in series.columns}\n",
    "series_dict_test = series_test.to_dict(orient=\"series\")\n",
    "exog_dict_test = {k: exog_test for k in series.columns}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot series\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "colors = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "fig, axs = plt.subplots(5, 1, figsize=(8, 4), sharex=True)\n",
    "for i, s in enumerate(series_dict.values()):\n",
    "    axs[i].plot(s, label=s.name, color=colors[i])\n",
    "    axs[i].legend(loc=\"upper right\", fontsize=8)\n",
    "    axs[i].tick_params(axis=\"both\", labelsize=8)\n",
    "    axs[i].axvline(\n",
    "        pd.to_datetime(end_train), color=\"white\", linestyle=\"--\", linewidth=1\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6b9a3fff3a345178ba02179f7793aa1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paramns: {'encoding': 'ordinal', 'interval': [5, 95], 'n_boot': 10}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paramns: {'encoding': 'onehot', 'interval': [5, 95], 'n_boot': 10}\n",
      "Paramns: {'encoding': 'ordinal_category', 'interval': [5, 95], 'n_boot': 10}\n"
     ]
    }
   ],
   "source": [
    "# Test predictions\n",
    "# ==============================================================================\n",
    "params = {\n",
    "    \"encoding\": [\"ordinal\", \"onehot\", \"ordinal_category\"],\n",
    "    \"interval\": [[5, 95]],\n",
    "    \"n_boot\": [10],\n",
    "}\n",
    "\n",
    "params_grid = list(ParameterGrid(params))\n",
    "\n",
    "for params in tqdm(params_grid):\n",
    "    print(f\"Paramns: {params}\")\n",
    "\n",
    "    steps = 10\n",
    "    forecaster = ForecasterRecursiveMultiSeries(\n",
    "        estimator=LinearRegression(),\n",
    "        lags=14,\n",
    "        encoding=params[\"encoding\"],\n",
    "        dropna_from_series=False,\n",
    "        transformer_series=StandardScaler(),\n",
    "        transformer_exog=StandardScaler(),\n",
    "    )\n",
    "\n",
    "    forecaster.fit(series=series_train, exog=exog_train)\n",
    "    predictions_1 = forecaster.predict(steps=steps, exog=exog_test)\n",
    "    predictions_1_interval = forecaster.predict_interval(\n",
    "        steps=steps,\n",
    "        exog=exog_test,\n",
    "        interval=params[\"interval\"],\n",
    "        n_boot=params[\"n_boot\"],\n",
    "    )\n",
    "\n",
    "    forecaster.fit(series=series_train, exog=exog_dict_train)\n",
    "    predictions_2 = forecaster.predict(steps=steps, exog=exog_dict_test)\n",
    "    predictions_2_interval = forecaster.predict_interval(\n",
    "        steps=steps,\n",
    "        exog=exog_dict_test,\n",
    "        interval=params[\"interval\"],\n",
    "        n_boot=params[\"n_boot\"],\n",
    "    )\n",
    "\n",
    "    forecaster.fit(series=series_dict_train, exog=exog_dict_train)\n",
    "    predictions_3 = forecaster.predict(steps=steps, exog=exog_dict_test)\n",
    "    predictions_3_interval = forecaster.predict_interval(\n",
    "        steps=steps,\n",
    "        exog=exog_dict_test,\n",
    "        interval=params[\"interval\"],\n",
    "        n_boot=params[\"n_boot\"],\n",
    "    )\n",
    "\n",
    "    pd.testing.assert_frame_equal(predictions_1, predictions_2)\n",
    "    pd.testing.assert_frame_equal(predictions_1, predictions_3)\n",
    "    pd.testing.assert_frame_equal(predictions_1_interval, predictions_2_interval)\n",
    "    pd.testing.assert_frame_equal(predictions_1_interval, predictions_3_interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d59a3b0ea154284b2fa790c73dd5eb5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/384 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 49\u001b[0m\n\u001b[1;32m     23\u001b[0m forecaster \u001b[38;5;241m=\u001b[39m ForecasterAutoregMultiSeries(\n\u001b[1;32m     24\u001b[0m     estimator\u001b[38;5;241m=\u001b[39mLinearRegression(),  \u001b[38;5;66;03m# LGBMRegressor(n_estimators=10, random_state=123, verbose=-1),\u001b[39;00m\n\u001b[1;32m     25\u001b[0m     lags\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     29\u001b[0m     transformer_exog\u001b[38;5;241m=\u001b[39mStandardScaler(),\n\u001b[1;32m     30\u001b[0m )\n\u001b[1;32m     32\u001b[0m metrics_1, predictions_1 \u001b[38;5;241m=\u001b[39m backtesting_forecaster_multiseries(\n\u001b[1;32m     33\u001b[0m     forecaster\u001b[38;5;241m=\u001b[39mforecaster,\n\u001b[1;32m     34\u001b[0m     series\u001b[38;5;241m=\u001b[39mseries,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     46\u001b[0m     show_progress\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m     47\u001b[0m )\n\u001b[0;32m---> 49\u001b[0m metrics_2, predictions_2 \u001b[38;5;241m=\u001b[39m \u001b[43mbacktesting_forecaster_multiseries\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     50\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforecaster\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforecaster\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     51\u001b[0m \u001b[43m    \u001b[49m\u001b[43mseries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mseries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexog_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     53\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlevels\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m    \u001b[49m\u001b[43msteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m24\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmetric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetrics\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[43m    \u001b[49m\u001b[43minitial_train_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minitial_train_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     57\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfixed_train_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfixed_train_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     58\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgap\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_incomplete_fold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow_incomplete_fold\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     60\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrefit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrefit\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     61\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mauto\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     62\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     63\u001b[0m \u001b[43m    \u001b[49m\u001b[43mshow_progress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m metrics_3, predictions_3 \u001b[38;5;241m=\u001b[39m backtesting_forecaster_multiseries(\n\u001b[1;32m     67\u001b[0m     forecaster\u001b[38;5;241m=\u001b[39mforecaster,\n\u001b[1;32m     68\u001b[0m     series\u001b[38;5;241m=\u001b[39mseries_dict,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     80\u001b[0m     show_progress\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m     81\u001b[0m )\n\u001b[1;32m     83\u001b[0m pd\u001b[38;5;241m.\u001b[39mtesting\u001b[38;5;241m.\u001b[39massert_frame_equal(\n\u001b[1;32m     84\u001b[0m     metrics_1,\n\u001b[1;32m     85\u001b[0m     metrics_2,\n\u001b[1;32m     86\u001b[0m )\n",
      "File \u001b[0;32m~/varios/skforecast/skforecast/model_selection_multiseries/model_selection_multiseries.py:792\u001b[0m, in \u001b[0;36mbacktesting_forecaster_multiseries\u001b[0;34m(forecaster, series, steps, metric, initial_train_size, fixed_train_size, gap, allow_incomplete_fold, levels, exog, refit, interval, n_boot, random_state, in_sample_residuals, n_jobs, verbose, show_progress, suppress_warnings)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    766\u001b[0m         (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`forecaster` must be of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmulti_series_forecasters\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    767\u001b[0m          \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor all other types of forecasters use the functions available in \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    768\u001b[0m          \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe `model_selection` module. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mforecaster_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    769\u001b[0m     )\n\u001b[1;32m    771\u001b[0m check_backtesting_input(\n\u001b[1;32m    772\u001b[0m     forecaster            \u001b[38;5;241m=\u001b[39m forecaster,\n\u001b[1;32m    773\u001b[0m     steps                 \u001b[38;5;241m=\u001b[39m steps,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    789\u001b[0m     suppress_warnings     \u001b[38;5;241m=\u001b[39m suppress_warnings\n\u001b[1;32m    790\u001b[0m )\n\u001b[0;32m--> 792\u001b[0m metrics_levels, backtest_predictions \u001b[38;5;241m=\u001b[39m \u001b[43m_backtesting_forecaster_multiseries\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    793\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforecaster\u001b[49m\u001b[43m            \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mforecaster\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mseries\u001b[49m\u001b[43m                \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mseries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43msteps\u001b[49m\u001b[43m                 \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlevels\u001b[49m\u001b[43m                \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmetric\u001b[49m\u001b[43m                \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmetric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43minitial_train_size\u001b[49m\u001b[43m    \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43minitial_train_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfixed_train_size\u001b[49m\u001b[43m      \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfixed_train_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgap\u001b[49m\u001b[43m                   \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mgap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    801\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_incomplete_fold\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mallow_incomplete_fold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexog\u001b[49m\u001b[43m                  \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mexog\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    803\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrefit\u001b[49m\u001b[43m                 \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mrefit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    804\u001b[0m \u001b[43m    \u001b[49m\u001b[43minterval\u001b[49m\u001b[43m              \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43minterval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    805\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_boot\u001b[49m\u001b[43m                \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mn_boot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    806\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m          \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    807\u001b[0m \u001b[43m    \u001b[49m\u001b[43min_sample_residuals\u001b[49m\u001b[43m   \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43min_sample_residuals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    808\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m                \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    809\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m               \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    810\u001b[0m \u001b[43m    \u001b[49m\u001b[43mshow_progress\u001b[49m\u001b[43m         \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mshow_progress\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    811\u001b[0m \u001b[43m    \u001b[49m\u001b[43msuppress_warnings\u001b[49m\u001b[43m     \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msuppress_warnings\u001b[49m\n\u001b[1;32m    812\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    814\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m metrics_levels, backtest_predictions\n",
      "File \u001b[0;32m~/varios/skforecast/skforecast/model_selection_multiseries/model_selection_multiseries.py:565\u001b[0m, in \u001b[0;36m_backtesting_forecaster_multiseries\u001b[0;34m(forecaster, series, steps, metric, initial_train_size, fixed_train_size, gap, allow_incomplete_fold, levels, exog, refit, interval, n_boot, random_state, in_sample_residuals, n_jobs, verbose, show_progress, suppress_warnings)\u001b[0m\n\u001b[1;32m    561\u001b[0m         pred \u001b[38;5;241m=\u001b[39m pred\u001b[38;5;241m.\u001b[39miloc[gap:, ]\n\u001b[1;32m    563\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m pred\n\u001b[0;32m--> 565\u001b[0m backtest_predictions \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    566\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_predict_forecaster\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    567\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata_fold\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdata_fold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    568\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforecaster\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mforecaster\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    569\u001b[0m \u001b[43m        \u001b[49m\u001b[43minterval\u001b[49m\u001b[43m   \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43minterval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    570\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevels\u001b[49m\u001b[43m     \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    571\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    572\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata_fold\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata_folds\u001b[49m\n\u001b[1;32m    573\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    575\u001b[0m backtest_predictions \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat(backtest_predictions, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    577\u001b[0m levels_in_backtest_predictions \u001b[38;5;241m=\u001b[39m backtest_predictions\u001b[38;5;241m.\u001b[39mcolumns\n",
      "File \u001b[0;32m~/anaconda3/envs/skforecast_12_py12/lib/python3.12/site-packages/joblib/parallel.py:1952\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1946\u001b[0m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[1;32m   1947\u001b[0m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[1;32m   1948\u001b[0m \u001b[38;5;66;03m# reach the first `yield` statement. This starts the aynchronous\u001b[39;00m\n\u001b[1;32m   1949\u001b[0m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[1;32m   1950\u001b[0m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 1952\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/skforecast_12_py12/lib/python3.12/site-packages/joblib/parallel.py:1595\u001b[0m, in \u001b[0;36mParallel._get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m   1592\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[1;32m   1594\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1595\u001b[0m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_retrieve()\n\u001b[1;32m   1597\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[1;32m   1598\u001b[0m     \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[1;32m   1599\u001b[0m     \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[1;32m   1600\u001b[0m     \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[1;32m   1601\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/skforecast_12_py12/lib/python3.12/site-packages/joblib/parallel.py:1707\u001b[0m, in \u001b[0;36mParallel._retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1702\u001b[0m \u001b[38;5;66;03m# If the next job is not ready for retrieval yet, we just wait for\u001b[39;00m\n\u001b[1;32m   1703\u001b[0m \u001b[38;5;66;03m# async callbacks to progress.\u001b[39;00m\n\u001b[1;32m   1704\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ((\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m   1705\u001b[0m     (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mget_status(\n\u001b[1;32m   1706\u001b[0m         timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout) \u001b[38;5;241m==\u001b[39m TASK_PENDING)):\n\u001b[0;32m-> 1707\u001b[0m     \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1708\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m   1710\u001b[0m \u001b[38;5;66;03m# We need to be careful: the job list can be filling up as\u001b[39;00m\n\u001b[1;32m   1711\u001b[0m \u001b[38;5;66;03m# we empty it and Python list are not thread-safe by\u001b[39;00m\n\u001b[1;32m   1712\u001b[0m \u001b[38;5;66;03m# default hence the use of the lock\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Test backtesting\n",
    "# ==============================================================================\n",
    "params = {\n",
    "    \"initial_train_size\": [50, len(series_train)],\n",
    "    \"refit\": [True, False, 2],\n",
    "    \"fixed_train_size\": [True, False],\n",
    "    \"gap\": [0, 7],\n",
    "    \"levels\": [\n",
    "        None,\n",
    "        [\"id_1000\", \"id_1001\", \"id_1002\", \"id_1003\", \"id_1004\"],\n",
    "        \"id_1000\",\n",
    "        [\"id_1000\", \"id_1001\"],\n",
    "    ],\n",
    "    \"metrics\": [[\"mean_absolute_error\", \"mean_squared_error\"], \"mean_absolute_error\"],\n",
    "    \"allow_incomplete_fold\": [True, False],\n",
    "}\n",
    "\n",
    "params_grid = list(ParameterGrid(params))\n",
    "\n",
    "for params in tqdm(params_grid):\n",
    "    print(f\"Paramns: {params}\")\n",
    "\n",
    "    forecaster = ForecasterRecursiveMultiSeries(\n",
    "        estimator=LinearRegression(),  # LGBMRegressor(n_estimators=10, random_state=123, verbose=-1),\n",
    "        lags=14,\n",
    "        encoding=\"ordinal\",\n",
    "        dropna_from_series=False,\n",
    "        transformer_series=StandardScaler(),\n",
    "        transformer_exog=StandardScaler(),\n",
    "    )\n",
    "\n",
    "    metrics_1, predictions_1 = backtesting_forecaster_multiseries(\n",
    "        forecaster=forecaster,\n",
    "        series=series,\n",
    "        exog=exog,\n",
    "        levels=params[\"levels\"],\n",
    "        steps=24,\n",
    "        metric=params[\"metrics\"],\n",
    "        initial_train_size=params[\"initial_train_size\"],\n",
    "        fixed_train_size=params[\"fixed_train_size\"],\n",
    "        gap=params[\"gap\"],\n",
    "        allow_incomplete_fold=params[\"allow_incomplete_fold\"],\n",
    "        refit=params[\"refit\"],\n",
    "        n_jobs=\"auto\",\n",
    "        verbose=False,\n",
    "        show_progress=False,\n",
    "    )\n",
    "\n",
    "    metrics_2, predictions_2 = backtesting_forecaster_multiseries(\n",
    "        forecaster=forecaster,\n",
    "        series=series,\n",
    "        exog=exog_dict,\n",
    "        levels=params[\"levels\"],\n",
    "        steps=24,\n",
    "        metric=params[\"metrics\"],\n",
    "        initial_train_size=params[\"initial_train_size\"],\n",
    "        fixed_train_size=params[\"fixed_train_size\"],\n",
    "        gap=params[\"gap\"],\n",
    "        allow_incomplete_fold=params[\"allow_incomplete_fold\"],\n",
    "        refit=params[\"refit\"],\n",
    "        n_jobs=\"auto\",\n",
    "        verbose=False,\n",
    "        show_progress=False,\n",
    "    )\n",
    "\n",
    "    metrics_3, predictions_3 = backtesting_forecaster_multiseries(\n",
    "        forecaster=forecaster,\n",
    "        series=series_dict,\n",
    "        exog=exog_dict,\n",
    "        levels=params[\"levels\"],\n",
    "        steps=24,\n",
    "        metric=params[\"metrics\"],\n",
    "        initial_train_size=params[\"initial_train_size\"],\n",
    "        fixed_train_size=params[\"fixed_train_size\"],\n",
    "        gap=params[\"gap\"],\n",
    "        allow_incomplete_fold=params[\"allow_incomplete_fold\"],\n",
    "        refit=params[\"refit\"],\n",
    "        n_jobs=\"auto\",\n",
    "        verbose=False,\n",
    "        show_progress=False,\n",
    "    )\n",
    "\n",
    "    pd.testing.assert_frame_equal(\n",
    "        metrics_1,\n",
    "        metrics_2,\n",
    "    )\n",
    "    pd.testing.assert_frame_equal(predictions_1, predictions_2)\n",
    "    pd.testing.assert_frame_equal(metrics_1, metrics_3)\n",
    "    pd.testing.assert_frame_equal(predictions_1, predictions_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': True, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': True, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 0, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 50, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': None, 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004'], 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': 'id_1000', 'metrics': 'mean_absolute_error', 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': ['mean_absolute_error', 'mean_squared_error'], 'refit': 2}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': True}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': False}\n",
      "Paramns: {'allow_incomplete_fold': False, 'fixed_train_size': False, 'gap': 7, 'initial_train_size': 305, 'levels': ['id_1000', 'id_1001'], 'metrics': 'mean_absolute_error', 'refit': 2}\n"
     ]
    }
   ],
   "source": [
    "# Test backtesting with intervals\n",
    "# ==============================================================================\n",
    "params = {\n",
    "    \"initial_train_size\": [50, len(series_train)],\n",
    "    \"refit\": [True, False, 2],\n",
    "    \"fixed_train_size\": [True, False],\n",
    "    \"gap\": [0, 7],\n",
    "    \"levels\": [\n",
    "        None,\n",
    "        [\"id_1000\", \"id_1001\", \"id_1002\", \"id_1003\", \"id_1004\"],\n",
    "        \"id_1000\",\n",
    "        [\"id_1000\", \"id_1001\"],\n",
    "    ],\n",
    "    \"metrics\": [[\"mean_absolute_error\", \"mean_squared_error\"], \"mean_absolute_error\"],\n",
    "    \"allow_incomplete_fold\": [True, False],\n",
    "}\n",
    "\n",
    "params_grid = list(ParameterGrid(params))\n",
    "\n",
    "for params in tqdm(params_grid):\n",
    "    print(f\"Paramns: {params}\")\n",
    "\n",
    "    forecaster = ForecasterRecursiveMultiSeries(\n",
    "        estimator=LinearRegression(),  # LGBMRegressor(n_estimators=10, random_state=123, verbose=-1),\n",
    "        lags=14,\n",
    "        encoding=\"ordinal\",\n",
    "        dropna_from_series=False,\n",
    "        transformer_series=StandardScaler(),\n",
    "        transformer_exog=StandardScaler(),\n",
    "    )\n",
    "\n",
    "    metrics_1, predictions_1 = backtesting_forecaster_multiseries(\n",
    "        forecaster=forecaster,\n",
    "        series=series,\n",
    "        exog=exog,\n",
    "        levels=params[\"levels\"],\n",
    "        steps=24,\n",
    "        interval=[5, 95],\n",
    "        n_boot=5,\n",
    "        metric=params[\"metrics\"],\n",
    "        initial_train_size=params[\"initial_train_size\"],\n",
    "        fixed_train_size=params[\"fixed_train_size\"],\n",
    "        gap=params[\"gap\"],\n",
    "        allow_incomplete_fold=params[\"allow_incomplete_fold\"],\n",
    "        refit=params[\"refit\"],\n",
    "        n_jobs=\"auto\",\n",
    "        verbose=False,\n",
    "        show_progress=False,\n",
    "    )\n",
    "\n",
    "    metrics_2, predictions_2 = backtesting_forecaster_multiseries(\n",
    "        forecaster=forecaster,\n",
    "        series=series,\n",
    "        exog=exog_dict,\n",
    "        levels=params[\"levels\"],\n",
    "        steps=24,\n",
    "        interval=[5, 95],\n",
    "        n_boot=5,\n",
    "        metric=params[\"metrics\"],\n",
    "        initial_train_size=params[\"initial_train_size\"],\n",
    "        fixed_train_size=params[\"fixed_train_size\"],\n",
    "        gap=params[\"gap\"],\n",
    "        allow_incomplete_fold=params[\"allow_incomplete_fold\"],\n",
    "        refit=params[\"refit\"],\n",
    "        n_jobs=\"auto\",\n",
    "        verbose=False,\n",
    "        show_progress=False,\n",
    "    )\n",
    "\n",
    "    metrics_3, predictions_3 = backtesting_forecaster_multiseries(\n",
    "        forecaster=forecaster,\n",
    "        series=series_dict,\n",
    "        exog=exog_dict,\n",
    "        levels=params[\"levels\"],\n",
    "        steps=24,\n",
    "        interval=[5, 95],\n",
    "        n_boot=5,\n",
    "        metric=params[\"metrics\"],\n",
    "        initial_train_size=params[\"initial_train_size\"],\n",
    "        fixed_train_size=params[\"fixed_train_size\"],\n",
    "        gap=params[\"gap\"],\n",
    "        allow_incomplete_fold=params[\"allow_incomplete_fold\"],\n",
    "        refit=params[\"refit\"],\n",
    "        n_jobs=\"auto\",\n",
    "        verbose=False,\n",
    "        show_progress=False,\n",
    "    )\n",
    "\n",
    "    pd.testing.assert_frame_equal(metrics_1, metrics_2)\n",
    "    pd.testing.assert_frame_equal(predictions_1, predictions_2)\n",
    "    pd.testing.assert_frame_equal(metrics_1, metrics_3)\n",
    "    pd.testing.assert_frame_equal(predictions_1, predictions_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of models compared: 3,\n",
      "         3 bayesian search in each lag configuration.\n",
      "[LightGBM] [Warning] Unknown parameter: lags\n",
      "Number of models compared: 3,\n",
      "         3 bayesian search in each lag configuration.\n",
      "Number of models compared: 3,\n",
      "         3 bayesian search in each lag configuration.\n"
     ]
    }
   ],
   "source": [
    "# Test Bayesian search\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursiveMultiSeries(\n",
    "    estimator=LGBMRegressor(n_estimators=10, random_state=123, verbose=-1),\n",
    "    lags=14,\n",
    "    encoding=\"ordinal\",\n",
    "    dropna_from_series=False,\n",
    "    transformer_series=StandardScaler(),\n",
    "    transformer_exog=StandardScaler(),\n",
    ")\n",
    "\n",
    "lags_grid = [[5], [1, 7, 14]]\n",
    "\n",
    "\n",
    "def search_space(trial):\n",
    "    search_space = {\n",
    "        \"n_estimators\": trial.suggest_int(\"n_estimators\", 2, 5),\n",
    "        \"max_depth\": trial.suggest_int(\"max_depth\", 2, 5),\n",
    "        \"lags\": trial.suggest_categorical(\"lags\", lags_grid),\n",
    "    }\n",
    "\n",
    "    return search_space\n",
    "\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"optuna\")\n",
    "\n",
    "    results_search_1, best_trial_1 = bayesian_search_forecaster_multiseries(\n",
    "        forecaster=forecaster,\n",
    "        series=series,\n",
    "        exog=exog,\n",
    "        search_space=search_space,\n",
    "        metric=\"mean_absolute_error\",\n",
    "        initial_train_size=len(series_train),\n",
    "        steps=10,\n",
    "        refit=False,\n",
    "        n_trials=3,\n",
    "        return_best=False,\n",
    "        show_progress=False,\n",
    "        verbose=False,\n",
    "    )\n",
    "\n",
    "    results_search_2, best_trial_2 = bayesian_search_forecaster_multiseries(\n",
    "        forecaster=forecaster,\n",
    "        series=series,\n",
    "        exog=exog_dict,\n",
    "        search_space=search_space,\n",
    "        metric=\"mean_absolute_error\",\n",
    "        initial_train_size=len(series_train),\n",
    "        steps=10,\n",
    "        refit=False,\n",
    "        n_trials=3,\n",
    "        return_best=False,\n",
    "        show_progress=False,\n",
    "        verbose=False,\n",
    "    )\n",
    "\n",
    "    results_search_3, best_trial_3 = bayesian_search_forecaster_multiseries(\n",
    "        forecaster=forecaster,\n",
    "        series=series_dict,\n",
    "        exog=exog_dict,\n",
    "        search_space=search_space,\n",
    "        metric=\"mean_absolute_error\",\n",
    "        initial_train_size=len(series_train),\n",
    "        steps=10,\n",
    "        refit=False,\n",
    "        n_trials=3,\n",
    "        return_best=False,\n",
    "        show_progress=False,\n",
    "        verbose=False,\n",
    "    )\n",
    "\n",
    "pd.testing.assert_frame_equal(results_search_1, results_search_2)\n",
    "pd.testing.assert_frame_equal(results_search_1, results_search_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 models compared for 5 level(s). Number of iterations: 8.\n",
      "8 models compared for 5 level(s). Number of iterations: 8.\n",
      "8 models compared for 5 level(s). Number of iterations: 8.\n"
     ]
    }
   ],
   "source": [
    "# Test Grid Search\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursiveMultiSeries(\n",
    "    estimator=LGBMRegressor(n_estimators=10, random_state=123, verbose=-1),\n",
    "    lags=14,\n",
    "    encoding=\"ordinal\",\n",
    "    dropna_from_series=False,\n",
    "    transformer_series=StandardScaler(),\n",
    ")\n",
    "\n",
    "lags_grid = [[5], [1, 7, 14]]\n",
    "\n",
    "param_grid = {\n",
    "    \"learning_rate\": [0.1],\n",
    "    \"n_estimators\": [10, 20],\n",
    "    \"max_depth\": [2, 5],\n",
    "}\n",
    "\n",
    "\n",
    "results_search_1 = grid_search_forecaster_multiseries(\n",
    "    forecaster=forecaster,\n",
    "    series=series,\n",
    "    exog=exog,\n",
    "    lags_grid=lags_grid,\n",
    "    param_grid=param_grid,\n",
    "    metric=\"mean_absolute_error\",\n",
    "    initial_train_size=len(series_train),\n",
    "    steps=10,\n",
    "    refit=False,\n",
    "    return_best=False,\n",
    "    show_progress=False,\n",
    "    verbose=False,\n",
    ")\n",
    "\n",
    "\n",
    "results_search_2 = grid_search_forecaster_multiseries(\n",
    "    forecaster=forecaster,\n",
    "    series=series,\n",
    "    exog=exog_dict,\n",
    "    lags_grid=lags_grid,\n",
    "    param_grid=param_grid,\n",
    "    metric=\"mean_absolute_error\",\n",
    "    initial_train_size=len(series_train),\n",
    "    steps=10,\n",
    "    refit=False,\n",
    "    return_best=False,\n",
    "    show_progress=False,\n",
    "    verbose=False,\n",
    ")\n",
    "\n",
    "results_search_3 = grid_search_forecaster_multiseries(\n",
    "    forecaster=forecaster,\n",
    "    series=series_dict,\n",
    "    exog=exog_dict,\n",
    "    lags_grid=lags_grid,\n",
    "    param_grid=param_grid,\n",
    "    metric=\"mean_absolute_error\",\n",
    "    initial_train_size=len(series_train),\n",
    "    steps=10,\n",
    "    refit=False,\n",
    "    return_best=False,\n",
    "    show_progress=False,\n",
    "    verbose=False,\n",
    ")\n",
    "\n",
    "pd.testing.assert_frame_equal(results_search_1, results_search_2)\n",
    "pd.testing.assert_frame_equal(results_search_1, results_search_3)"
   ]
  }
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